1. Field of the Invention
The present invention relates generally to spoken language understanding and more particularly to exploiting unlabeled utterances for spoken language understanding.
2. Introduction
Voice-based natural dialog systems enable customers to express what they want in spoken natural language. Such systems automatically extract the meaning from speech input and act upon what people actually say, in contrast to what one would like them to say, shifting the burden from the users to the machine. In a natural language spoken dialog system, identifying the speaker's intent can be seen as a general classification problem. Once the speaker's intent is determined, the natural language spoken dialog system can take actions accordingly to satisfy their request.
In a natural language spoken dialog system, the speaker's utterance is recognized first using an automatic speech recognizer component. Then, the intent of the speaker is identified (or classified) from the recognized speech sequence, using a natural language understanding component.
This classification process can be seen as a general classification problem. In one example, this classification problem is applied to call routing. In this call classification example, consider the utterance “I would like to learn my account balance” in a customer-care application. Assuming that the utterance is recognized correctly, the corresponding intent or the calltype would be Account Balance Request and the action would be prompting the balance to the user or routing this call to the billing department.
When statistical classifiers are employed, they are trained using large amounts of task data, which is transcribed and labeled by humans. This transcription and labeling process is a very expensive and laborious process. By “labeling,” we mean assigning one or more predefined classification types to each utterance. It is clear that the bottleneck in building an accurate statistical system is the time spent for high-quality labeling. What is needed therefore is the ability to build better statistical classification systems in a shorter time frame.